Algorithmic investing reinvented.

Generated Code

# Setup logging configuration
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

class ExampleStrategy(bt.Strategy):
    params = (
        ('low_ibs', 0.2),  # Examples of Low IBS thresholds
        ('high_ibs', 0.8),  # Examples of High IBS thresholds
        ('exit_days', 30),  # Holding period after entry
    )

    def __init__(self):
        self.order = None  # Keep track of pending orders
        self.entry_date = None  # Track the entry date to check hold duration
        logging.info('Internal Bar Strength strategy initialized.')

    def calculate_ibs(self, data):
        return (data.close[0] - data.low[0]) / (data.high[0] - data.low[0])

    def next(self):
        current_ibs = self.calculate_ibs(self.datas[0])
        close_price = self.datas[0].close[0]
        current_date = self.datas[0].datetime.date(0)

        # Check for buy condition
        if not self.position and current_ibs < self.params.low_ibs:
            size = self.broker.get_cash() / close_price
            self.buy(size=size)
            self.entry_date = current_date
            logging.info(f'BUY : IBS < {self.params.low_ibs} | Date: {current_date} | IBS: {current_ibs} | Size: {size} | Portfolio Value: {self.broker.getvalue()}')

        # Check for sell condition
        elif self.position:
            holding_period = (current_date - self.entry_date).days
            if current_ibs > self.params.high_ibs or holding_period >= self.params.exit_days:
                self.sell(size=self.position.size)
                self.entry_date = None  # Reset entry date
                logging.info(f'SELL : IBS > {self.params.high_ibs} or 30 days reached | Date: {current_date} | IBS: {current_ibs} | Size: {self.position.size} | Portfolio Value: {self.broker.getvalue()}')

# Define the lookback period for indicators, here we choose a minimal lookback as IBS is calculated daily
lookback_period = 1

# Fetch and add data to Cerebro, make sure to adjust stock tickers and timeframe
nq_data = bt.feeds.PandasData(dataname=yf.download('^NDX', start_date, end_date), name='US100')
cerebro.adddata(nq_data)

globals()['lookback_period'] = lookback_period  # Update global variable for lookback period usage